system

Abstract

The system according to this embodiment aims to protect privacy by notifying individuals when their facial photographs are uploaded to the internet. [Solution] The system according to the embodiment comprises a registration unit, a monitoring unit, a detection unit, a notification unit, an authorization unit, and a blocking unit. The registration unit registers 3D data of the user's face. The monitoring unit monitors the internet. The detection unit detects face photos detected by the monitoring unit. The notification unit notifies the user of the information detected by the detection unit. The authorization unit accepts the user's authorization based on the information notified by the notification unit. The blocking unit blocks posts that have not been authorized by the authorization unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a risk that an individual's face photo is uploaded without permission on the Internet, and there are problems in privacy protection.

[0005] The system according to the embodiment aims to notify the person when an individual's face photo is uploaded on the Internet and protect privacy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, a monitoring unit, a detection unit, a notification unit, an authorization unit, and a blocking unit. The registration unit registers 3D data of the user's face. The monitoring unit monitors the internet. The detection unit detects face photographs detected by the monitoring unit. The notification unit notifies the user of the information detected by the detection unit. The authorization unit accepts the user's authorization based on the information notified by the notification unit. The blocking unit blocks posts that have not been authorized by the authorization unit. [Effects of the Invention]

[0007] The system according to this embodiment can protect privacy by notifying the individual when their facial photograph is uploaded to the internet. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention combines AI and NFT technologies to notify individuals when their facial photograph is about to be uploaded to the internet. This system registers the user's 3D facial data on a platform and constantly monitors the internet to check for uploads of the user's face. If detected, the system immediately sends a notification to the user. The notification includes information on who is attempting to post the user's facial photograph to which site. The user receives the notification and can choose whether or not to allow the posting. If the user selects "no," the system blocks the posting, preventing the user's facial photograph from being published on the internet. This mechanism allows users to use the internet with peace of mind. Furthermore, this mechanism can protect users from malicious use, such as deepfakes and advertisements for fraudulent products. In addition, collecting 3D facial data opens up new business possibilities. For example, it could be used in genetic research, the beauty industry, or the security industry. In this way, the system can protect user privacy and provide an environment in which users can use the internet with peace of mind.

[0029] The system according to this embodiment comprises a registration unit, a monitoring unit, a detection unit, a notification unit, an authorization unit, and a blocking unit. The registration unit registers 3D data of the user's face. The registration unit allows, for example, a user to upload their 3D data of their face to the platform. The registration unit can also store the user's 3D data of their face as an NFT on the blockchain. For example, the registration unit stores the user's 3D data of their face as an NFT with unique value, making it difficult to tamper with. The monitoring unit monitors the internet. The monitoring unit constantly monitors sites such as social networking services and blogs to check whether the user's face photo has been uploaded. The monitoring unit uses AI to analyze vast amounts of data on the internet in real time and detects the user's face photo. The detection unit detects the face photo detected by the monitoring unit. The detection unit can, for example, use AI to detect the user's face photo with high accuracy. The detection unit uses face recognition technology to identify and detect the user's face photo. The notification unit notifies the user of the information detected by the detection unit. The notification unit sends notifications to the user, for example, using email or app notifications. The notification unit sends notifications that include information about who is trying to post the user's face photo on which site. The permission unit accepts the user's permission based on the information notified by the notification unit. The permission unit provides, for example, an interface that allows the user to choose whether or not to allow the post. The permission unit accepts permission for the post when the user selects "yes" or "no". The blocking unit blocks posts that have not been permitted by the permission unit. The blocking unit blocks a post, for example, if the user selects "no". The blocking unit uses AI to prevent the user's face photo from being published on the internet. As a result, the system according to the embodiment can protect the user's privacy and provide an environment in which they can use the internet with peace of mind.

[0030] The registration unit registers 3D data of users' faces. For example, users can upload their own 3D facial data to the platform. Specifically, users use a dedicated application to scan their faces and generate 3D data. This 3D data captures the shape and features of the face in detail, enabling highly accurate facial recognition. Furthermore, the registration unit can also store the user's 3D facial data as an NFT on the blockchain. This makes data tampering difficult, improving the reliability and security of the user's facial data. For example, the registration unit stores the user's 3D facial data as a unique and valuable NFT, making it difficult to tamper with. This allows users to confirm that their facial data is securely stored and use the system with peace of mind. The registration unit also minimizes the risk of data leakage by encrypting and storing the user's facial data. Additionally, the registration unit provides an interface that allows users to manage their facial data, enabling them to update or delete data as needed. This allows users to keep their data up-to-date at all times.

[0031] The monitoring unit monitors the internet. For example, the monitoring unit constantly monitors sites such as social media and blogs to check whether user photos have been uploaded. Specifically, the monitoring unit uses AI to analyze vast amounts of data on the internet in real time and detect user photos. The AI ​​uses image recognition technology to scan images on the internet and identify user photos. For example, the monitoring unit checks whether user photos are included in social media posts, blog articles, and images on news sites. The monitoring unit uses AI deep learning algorithms to analyze facial features with high accuracy and can quickly detect user photos. Furthermore, the monitoring unit automatically scans every time new content is uploaded to the internet to check whether it contains user photos. This allows the monitoring unit to detect user photos before they are published on the internet and to respond quickly. Also, if a user's photo is detected, the monitoring unit sends that information to the detection unit to proceed to the next step. This allows the monitoring unit to protect user privacy and prevent the misuse of facial photos on the internet.

[0032] The detection unit detects the facial photographs detected by the monitoring unit. The detection unit can detect user facial photographs with high accuracy, for example, by using AI. Specifically, the detection unit identifies and detects user facial photographs using facial recognition technology. The AI ​​utilizes a facial recognition algorithm based on deep learning to analyze facial features in the image and identify user facial photographs with high accuracy. For example, the detection unit analyzes the image data transmitted from the monitoring unit to check whether it contains a user's facial photograph. The detection unit analyzes the facial contours and features such as eyes, nose, and mouth in detail to identify the user's facial photograph. Furthermore, if a user's facial photograph is detected, the detection unit sends that information to the notification unit and proceeds to the next step. This allows the detection unit to detect user facial photographs with high accuracy before they are published on the internet and to respond quickly. In addition, the detection unit can improve the accuracy of facial photograph detection by utilizing past data and statistical information. For example, it can continuously train the facial recognition algorithm based on data of facial photographs detected in the past to improve detection accuracy. This allows the detection unit to always use the latest technology to perform highly accurate facial image detection, thereby protecting user privacy.

[0033] The notification unit notifies the user of information detected by the detection unit. The notification unit sends notifications to the user, for example, using email or app notifications. Specifically, the notification unit sends notifications in an appropriate manner based on the user's contact information. For example, it can send notifications to the email address registered by the user or send push notifications through a dedicated application. The notification unit sends notifications that include information on who is trying to post the user's face photo on which site. This allows the user to understand how their face photo is being used. Furthermore, the notification unit provides detailed information in the notification to help the user take appropriate action. For example, the notification may include the date and time the face photo was detected, the URL of the site, and information about the poster. This allows the user to respond quickly and take necessary measures. The notification unit can also collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, it can track the user's response after receiving a notification and identify areas for improvement in the notification content. This allows the notification unit to provide users with timely and accurate information and protect their privacy.

[0034] The permission unit accepts user permission based on information notified by the notification unit. For example, the permission unit provides an interface where users can choose whether or not to allow posting. Specifically, the permission unit provides an interface where users can accept permission to post by selecting "yes" or "no". For example, after a user receives a notification, they can choose to allow posting through a dedicated application or website. The permission unit records the user's selection and proceeds to the next step. This allows users to decide for themselves whether or not their face photo will be published on the internet. Furthermore, the permission unit manages the posting permission status based on the user's selection. For example, if the user selects "yes", the information is recorded and it is confirmed that posting has been permitted. Conversely, if the user selects "no", the information is recorded and it is confirmed that posting has not been permitted. This allows the permission unit to take appropriate action based on the user's selection and protect the user's privacy. The permission unit also provides a function to update selections in case the user wishes to change their selection. This allows users to review their selections at any time and change them as needed.

[0035] The blocking unit blocks posts that have not been permitted by the permission unit. For example, if a user selects "No," the blocking unit will block that post. Specifically, the blocking unit uses AI to prevent users' photos from being published on the internet. The AI ​​monitors internet posts, identifies posts containing users' photos, and blocks them. For example, the blocking unit requests sites such as social networking services and blogs to remove posts containing users' photos. The blocking unit also monitors internet posts in real time and responds quickly if a new post containing a user's photo is uploaded. This allows the blocking unit to prevent users' photos from being published on the internet and protect user privacy. Furthermore, the blocking unit verifies whether posts containing users' photos have been removed and notifies the user when the removal is complete. This allows users to confirm that their photos have been removed and use the internet with peace of mind. The blocking unit also has a function to respond if a removal request is refused. For example, if a removal request is refused, the blocking unit collects information for taking legal action and provides it to the user. This allows the blocking function to prevent the unauthorized use of users' facial images and provide comprehensive measures to protect user privacy.

[0036] The monitoring unit can monitor sites such as social networking services (SNS) and blogs. For example, the monitoring unit can constantly monitor SNS platforms and blog services to check whether user photos have been uploaded. The monitoring unit uses AI to analyze vast amounts of data on the internet in real time and detect user photos. This allows for broad coverage of places where user photos may be uploaded by monitoring sites such as SNS and blogs. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input data from SNS platforms and blog services into a generating AI and have the generating AI perform the detection of user photos.

[0037] The detection unit can detect information when a user's face photo is about to be posted. The detection unit can, for example, use AI to detect situations in which a user's face photo is about to be posted. For example, the detection unit can detect a user's face photo when the post button is pressed or when the post content is uploaded. This allows for a quick response by detecting information when a user's face photo is about to be posted. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input data on situations in which a post is about to be posted into a generating AI and have the generating AI perform the detection of the user's face photo.

[0038] The notification unit can notify the user of information about who is trying to post the user's face photo on which site. The notification unit sends notifications to the user, for example, using email or app notifications. The notification unit sends notifications that include information such as the poster's username and the URL of the site. This allows the user to take appropriate action by notifying the user of information about who is trying to post the user's face photo on which site. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the detected information into a generating AI and have the generating AI generate the notification content.

[0039] The permission unit can provide an interface that allows the user to choose whether or not to allow a post. For example, the permission unit can provide a web interface or a mobile app interface, and the user can choose "yes" or "no" to grant permission for the post. By providing an interface that allows the user to choose whether or not to allow a post, it becomes possible to respond in a way that reflects the user's wishes. Some or all of the above processing in the permission unit may be performed using AI or not. For example, the permission unit can input the user's selection data into a generating AI and have the generating AI make the permission decision.

[0040] The blocking unit can block a post if the user selects "No". For example, if the user selects "No", the blocking unit will automatically block the post. The blocking unit uses AI to prevent the user's face photo from being published on the internet. This prevents the user's face photo from being published on the internet by blocking the post when the user selects "No". Some or all of the above processing in the blocking unit may be performed using AI or not. For example, the blocking unit can input the user's selection data into a generating AI and have the generating AI perform the blocking operation.

[0041] The registration unit can analyze the user's past facial photograph data and select the optimal method for generating 3D data. For example, the registration unit can select the clearest image from the user's past facial photograph data and generate 3D data. The registration unit can also analyze the user's facial features, select the optimal angle and expression, and generate 3D data. Furthermore, the registration unit can generate 3D data of the face under different lighting conditions based on the user's past facial photograph data. In this way, optimal 3D data can be generated by analyzing the user's past facial photograph data. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's past facial photograph data into a generation AI and have the generation AI select the optimal method for generating 3D data.

[0042] The registration unit can filter 3D facial data based on the user's current lifestyle and areas of interest. For example, if the user is interested in sports, the registration unit can apply sports-related filters to generate 3D data. Furthermore, if the user wishes to use the data in a business setting, the registration unit can generate 3D data reflecting formal expressions and attire. Additionally, if the user is interested in entertainment, the registration unit can generate 3D data reflecting cheerful expressions and poses. This allows for the generation of more appropriate 3D data by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the registration unit may be performed using AI, or not. For example, the registration unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0043] The registration unit can prioritize registering highly relevant data when registering 3D facial data, taking into account the user's geographical location. For example, if the user lives in a specific region, the registration unit can prioritize registering 3D data that matches the culture and customs of that region. Furthermore, if the user is traveling, the registration unit can prioritize registering 3D data that reflects the characteristics of the travel destination. Additionally, if the user is participating in a specific event, the registration unit can prioritize registering 3D data related to that event. This allows for the priority registration of highly relevant data by considering the user's geographical location. Some or all of the above processing in the registration unit may be performed using AI, or not. For example, the registration unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0044] The registration unit can analyze the user's social media activity and register relevant data when registering 3D facial data. For example, the registration unit can register relevant 3D data based on the content the user frequently posts on social media. The registration unit can also analyze the activity of the user's social media followers and friends and register relevant 3D data. Furthermore, if the user uses a specific hashtag, the registration unit can register 3D data related to that hashtag. In this way, relevant data can be registered by analyzing the user's social media activity. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input data on the user's social media activity into a generating AI and have the generating AI select relevant data.

[0045] The monitoring unit can adjust the monitoring intensity for specific sites or platforms during monitoring. For example, the monitoring unit can increase the monitoring intensity for social networking sites. It can also adjust the monitoring intensity for blogging platforms. Furthermore, the monitoring unit can appropriately set the monitoring intensity for video sharing sites. This allows for efficient monitoring by adjusting the monitoring intensity for specific sites or platforms. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input data from specific sites or platforms into a generating AI and have the generating AI perform the adjustment of the monitoring intensity.

[0046] The monitoring unit can analyze the user's past internet activity during monitoring and select the optimal monitoring method. For example, the monitoring unit can prioritize monitoring sites that the user frequently accesses. Furthermore, the monitoring unit can identify high-risk sites based on the user's past internet activity and strengthen monitoring accordingly. In addition, the monitoring unit can analyze patterns in the user's internet activity and set an optimal monitoring schedule. This allows for the selection of the optimal monitoring method by analyzing the user's past internet activity. Some or all of the above processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the user's past internet activity data into a generating AI and have the generating AI select the optimal monitoring method.

[0047] The monitoring unit can adjust its monitoring range while taking into account the user's geographical location information. For example, if the user is in a specific region, the monitoring unit will prioritize monitoring sites related to that region. Furthermore, if the user is traveling, the monitoring unit can include sites in the travel destination in its monitoring range. Additionally, if the user is participating in a specific event, the monitoring unit can include sites related to that event in its monitoring range. This allows for more relevant monitoring by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI adjust the monitoring range.

[0048] The monitoring unit can analyze a user's social media activity during monitoring to select targets for monitoring. For example, the monitoring unit may prioritize monitoring social media platforms to which the user frequently posts. It can also analyze the activity of the user's social media followers and friends and include related sites in its monitoring targets. Furthermore, if the user uses a specific hashtag, the monitoring unit can include sites related to that hashtag in its monitoring targets. This allows for the selection of relevant targets by analyzing the user's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input data on the user's social media activity into a generating AI and have the generating AI perform the selection of targets for monitoring.

[0049] The detection unit can improve detection accuracy by analyzing the features of the facial photograph in detail during detection. For example, the detection unit can improve detection accuracy by analyzing the contours and feature points of the face in detail. The detection unit can also improve detection accuracy by considering facial expressions and angles. Furthermore, the detection unit can analyze the lighting conditions of the face and apply the optimal detection algorithm. This improves detection accuracy by analyzing the features of the facial photograph in detail. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the feature data of the facial photograph into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0050] The detection unit can optimize its detection algorithm by referring to the user's past facial image data during detection. For example, the detection unit can select the optimal detection algorithm based on the user's past facial image data. The detection unit can also analyze the user's facial features and optimize the detection algorithm. Furthermore, the detection unit can improve detection accuracy by referring to the user's past facial image data. In this way, the detection algorithm can be optimized by referring to the user's past facial image data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the user's past facial image data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0051] The detection unit can adjust its detection range while considering the user's geographical location information. For example, if the user is in a specific region, the detection unit will prioritize detecting sites related to that region. Furthermore, if the user is traveling, the detection unit can also include sites in the travel destination in its detection range. Additionally, if the user is participating in a specific event, the detection unit can include sites related to that event in its detection range. This allows for highly relevant detection by considering the user's geographical location information. Some or all of the above processing in the detection unit may be performed using AI, or without AI. For example, the detection unit can input the user's geographical location information into a generating AI and have the generating AI adjust the detection range.

[0052] The detection unit can analyze the user's social media activity and select targets for detection during the detection process. For example, the detection unit may prioritize detecting social media platforms that the user frequently posts on. The detection unit can also analyze the activity of the user's social media followers and friends and include related sites in the detection targets. Furthermore, if the user uses a specific hashtag, the detection unit can also include sites related to that hashtag in the detection targets. This allows the detection unit to select relevant targets by analyzing the user's social media activity. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the selection of detection targets.

[0053] The notification unit can adjust the level of detail in the notification content based on the user's level of interest. For example, if the user shows a high level of interest, the notification unit will provide detailed notification content. Conversely, if the user shows a low level of interest, the notification unit can provide concise notification content. Furthermore, the notification unit can dynamically adjust the level of detail in the notification content according to the user's level of interest. This allows the notification unit to provide the user with information appropriate to their level of interest. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user interest data into a generating AI and have the generating AI perform the adjustment of the level of detail in the notification content.

[0054] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit will prioritize using notification methods that the user has preferred in the past (email, push notifications, etc.). The notification unit can also select the most effective notification method from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and set the optimal notification timing. This allows the notification unit to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0055] The notification unit can customize notification content by considering the user's geographical location when sending a notification. For example, if the user is in a specific region, the notification unit can include information related to that region in the notification. It can also include information about the travel destination if the user is traveling. Furthermore, if the user is participating in a specific event, the notification unit can include information related to that event in the notification. This allows for the provision of highly relevant notifications by considering the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI customize the notification content.

[0056] The notification unit can analyze the user's social media activity and adjust the notification content when sending a notification. For example, the notification unit can include relevant information in the notification based on the content the user frequently posts on social media. The notification unit can also analyze the activity of the user's social media followers and friends and include relevant information in the notification. Furthermore, if the notification unit uses a specific hashtag, it can include information related to that hashtag in the notification. This allows the notification unit to provide relevant notification content by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the user's social media activity into a generating AI and have the generating AI adjust the notification content.

[0057] The authorization unit can select the optimal authorization method by referring to the user's past authorization history when granting authorization. For example, the authorization unit can propose the optimal authorization method based on what the user has previously authorized. The authorization unit can also select the most effective authorization method from the user's past authorization history. Furthermore, the authorization unit can analyze the user's past authorization history and set the optimal authorization timing. This allows the authorization unit to select the optimal authorization method by referring to the user's past authorization history. Some or all of the above processing in the authorization unit may be performed using AI or not. For example, the authorization unit can input the user's past authorization history data into a generating AI and have the generating AI perform the selection of the optimal authorization method.

[0058] The permission unit can customize the permission process based on the user's current living situation when granting permission. For example, if the user is busy, the permission unit can provide a simplified permission process. If the user is relaxed, the permission unit can also provide a more detailed permission process. Furthermore, if the user is stressed, the permission unit can provide an intuitive and easy-to-use permission process. This allows the permission unit to provide an appropriate permission process based on the user's current living situation. Some or all of the above processing in the permission unit may be performed using AI or not. For example, the permission unit can input user living situation data into a generating AI and have the generating AI perform the customization of the permission process.

[0059] The permission unit can customize the permissions given, taking into account the user's geographical location. For example, if the user is in a specific region, the permission unit can provide permissions related to that region. Furthermore, if the user is traveling, the permission unit can include information about the travel destination in the permissions. Additionally, if the user is participating in a specific event, the permission unit can provide permissions related to that event. This allows for the provision of highly relevant permissions by considering the user's geographical location. Some or all of the above processing in the permission unit may be performed using AI or not. For example, the permission unit can input the user's geographical location into a generating AI and have the generating AI customize the permissions.

[0060] The permission unit can analyze the user's social media activity and adjust the permissions when granting access. For example, the permission unit can provide relevant permissions based on the content the user frequently posts on social media. It can also analyze the activity of the user's social media followers and friends and provide relevant permissions. Furthermore, if the permission unit uses a specific hashtag, it can provide permissions related to that hashtag. In this way, relevant permissions can be provided by analyzing the user's social media activity. Some or all of the above processing in the permission unit may be performed using AI or not. For example, the permission unit can input data on the user's social media activity into a generating AI and have the generating AI perform the adjustment of permissions.

[0061] The blocking unit can select the optimal blocking method by referring to the user's past blocking history when blocking. For example, the blocking unit can suggest the optimal blocking method based on the content the user has blocked in the past. The blocking unit can also select the most effective blocking method from the user's past blocking history. Furthermore, the blocking unit can analyze the user's past blocking history and set the optimal blocking timing. This allows the optimal blocking method to be selected by referring to the user's past blocking history. Some or all of the above processing in the blocking unit may be performed using AI or not. For example, the blocking unit can input the user's past blocking history data into a generating AI and have the generating AI select the optimal blocking method.

[0062] The blocking unit can customize the blocking method based on the user's current life situation when blocking. For example, if the user is busy, the blocking unit can perform blocking quickly. It can also provide detailed blocking options if the user is relaxed. Furthermore, if the user is stressed, the blocking unit can provide intuitive and easy-to-use blocking methods. This allows the blocking unit to provide the appropriate blocking method for the user by customizing it based on the user's current life situation. Some or all of the above processing in the blocking unit may be performed using AI or not. For example, the blocking unit can input user life situation data into a generating AI and have the generating AI perform the customization of the blocking method.

[0063] The block section can customize the content of a block by considering the user's geographical location information when blocking. For example, if the user is in a specific region, the block section can provide block content related to that region. Furthermore, if the user is traveling, the block section can include information about the travel destination in the block content. Additionally, if the user is participating in a specific event, the block section can provide block content related to that event. This allows for the provision of highly relevant block content by considering the user's geographical location information. Some or all of the above processing in the block section may be performed using AI, or not. For example, the block section can input the user's geographical location information into a generating AI and have the generating AI perform the customization of the block content.

[0064] The blocking unit can analyze the user's social media activity and adjust the blocking content when blocking. For example, the blocking unit can provide relevant blocking content based on the content the user frequently posts on social media. It can also analyze the activity of the user's social media followers and friends and provide relevant blocking content. Furthermore, if the blocking unit uses a specific hashtag, it can provide blocking content related to that hashtag. In this way, relevant blocking content can be provided by analyzing the user's social media activity. Some or all of the above processing in the blocking unit may be performed using AI or not. For example, the blocking unit can input data on the user's social media activity into a generating AI and have the generating AI perform the adjustment of the blocking content.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The registration unit can analyze the user's past facial photograph data and select the optimal method for generating 3D data when registering 3D data of the user's face. For example, it can select the clearest image from the user's past facial photograph data and generate 3D data from it. It can also analyze the user's facial features and select the optimal angle and expression to generate 3D data. Furthermore, it can generate 3D data of the face under different lighting conditions based on the user's past facial photograph data. In this way, optimal 3D data can be generated by analyzing the user's past facial photograph data. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's past facial photograph data into a generation AI and have the generation AI select the optimal method for generating 3D data.

[0067] The detection unit can improve detection accuracy by analyzing the features of the facial photograph in detail during detection. For example, it can improve detection accuracy by analyzing the contours and feature points of the face in detail. It can also improve detection accuracy by considering facial expressions and angles. Furthermore, it can analyze the lighting conditions of the face and apply the optimal detection algorithm. As a result, detection accuracy is improved by analyzing the features of the facial photograph in detail. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the feature data of the facial photograph into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0068] The monitoring unit can adjust the monitoring intensity for specific sites or platforms during monitoring. For example, it can increase the monitoring intensity for social networking sites. It can also adjust the monitoring intensity for blogging platforms. Furthermore, it can appropriately set the monitoring intensity for video sharing sites. This allows for efficient monitoring by adjusting the monitoring intensity for specific sites or platforms. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input data from specific sites or platforms into a generating AI and have the generating AI perform the adjustment of the monitoring intensity.

[0069] The detection unit can optimize its detection algorithm by referring to the user's past facial image data during detection. For example, it can select the optimal detection algorithm based on the user's past facial image data. It can also analyze the user's facial features and optimize the detection algorithm. Furthermore, it can improve detection accuracy by referring to the user's past facial image data. In this way, the detection algorithm can be optimized by referring to the user's past facial image data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the user's past facial image data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0070] The notification unit can adjust the level of detail in the notification content based on the user's level of interest. For example, if the user shows high interest, it can provide detailed notification content. Conversely, if the user shows low interest, it can provide concise notification content. Furthermore, the level of detail in the notification content can be dynamically adjusted according to the user's level of interest. This allows the system to provide the user with information appropriate to their needs by adjusting the level of detail in the notification content based on their level of interest. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user interest data into a generating AI and have the generating AI perform the adjustment of the level of detail in the notification content.

[0071] The authorization unit can select the optimal authorization method by referring to the user's past authorization history when granting authorization. For example, it can propose the optimal authorization method based on what the user has previously authorized. It can also select the most effective authorization method from the user's past authorization history. Furthermore, it can analyze the user's past authorization history and set the optimal authorization timing. This allows the system to select the optimal authorization method by referring to the user's past authorization history. Some or all of the above processing in the authorization unit may be performed using AI or not. For example, the authorization unit can input the user's past authorization history data into a generating AI and have the generating AI select the optimal authorization method.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The registration unit registers the user's 3D facial data. For example, a user can upload their own 3D facial data to the platform. The registration unit can also store the user's 3D facial data as an NFT on the blockchain. This ensures that the user's 3D facial data is stored as a unique and valuable NFT, making it difficult to tamper with. Step 2: The monitoring unit monitors the internet. For example, it constantly monitors sites such as social media and blogs to check whether user photos have been uploaded. The monitoring unit uses AI to analyze vast amounts of data on the internet in real time and detect user photos. Step 3: The detection unit detects the facial image detected by the monitoring unit. For example, AI can be used to detect the user's facial image with high accuracy. The detection unit identifies and detects the user's facial image using facial recognition technology. Step 4: The notification unit notifies the user of the information detected by the detection unit. For example, it sends a notification to the user using email or app notifications. The notification unit sends a notification that includes information about who is trying to post the user's face photo on which site. Step 5: The permission unit accepts user permission based on the information notified by the notification unit. For example, it provides an interface where the user can choose whether or not to allow the post. The user's selection of "yes" or "no" grants permission for the post. Step 6: The blocking unit blocks posts that were not permitted by the allowing unit. For example, if the user selects "No," the post will be blocked. The blocking unit uses AI to prevent the user's face photo from being published on the internet.

[0074] (Example of form 2) The system according to an embodiment of the present invention combines AI and NFT technologies to notify individuals when their facial photograph is about to be uploaded to the internet. This system registers the user's 3D facial data on a platform and constantly monitors the internet to check for uploads of the user's face. If detected, the system immediately sends a notification to the user. The notification includes information on who is attempting to post the user's facial photograph to which site. The user receives the notification and can choose whether or not to allow the posting. If the user selects "no," the system blocks the posting, preventing the user's facial photograph from being published on the internet. This mechanism allows users to use the internet with peace of mind. Furthermore, this mechanism can protect users from malicious use, such as deepfakes and advertisements for fraudulent products. In addition, collecting 3D facial data opens up new business possibilities. For example, it could be used in genetic research, the beauty industry, or the security industry. In this way, the system can protect user privacy and provide an environment in which users can use the internet with peace of mind.

[0075] The system according to this embodiment comprises a registration unit, a monitoring unit, a detection unit, a notification unit, an authorization unit, and a blocking unit. The registration unit registers 3D data of the user's face. The registration unit allows, for example, a user to upload their 3D data of their face to the platform. The registration unit can also store the user's 3D data of their face as an NFT on the blockchain. For example, the registration unit stores the user's 3D data of their face as an NFT with unique value, making it difficult to tamper with. The monitoring unit monitors the internet. The monitoring unit constantly monitors sites such as social networking services and blogs to check whether the user's face photo has been uploaded. The monitoring unit uses AI to analyze vast amounts of data on the internet in real time and detects the user's face photo. The detection unit detects the face photo detected by the monitoring unit. The detection unit can, for example, use AI to detect the user's face photo with high accuracy. The detection unit uses face recognition technology to identify and detect the user's face photo. The notification unit notifies the user of the information detected by the detection unit. The notification unit sends notifications to the user, for example, using email or app notifications. The notification unit sends notifications that include information about who is trying to post the user's face photo on which site. The permission unit accepts the user's permission based on the information notified by the notification unit. The permission unit provides, for example, an interface that allows the user to choose whether or not to allow the post. The permission unit accepts permission for the post when the user selects "yes" or "no". The blocking unit blocks posts that have not been permitted by the permission unit. The blocking unit blocks a post, for example, if the user selects "no". The blocking unit uses AI to prevent the user's face photo from being published on the internet. As a result, the system according to the embodiment can protect the user's privacy and provide an environment in which they can use the internet with peace of mind.

[0076] The registration unit registers 3D data of users' faces. For example, users can upload their own 3D facial data to the platform. Specifically, users use a dedicated application to scan their faces and generate 3D data. This 3D data captures the shape and features of the face in detail, enabling highly accurate facial recognition. Furthermore, the registration unit can also store the user's 3D facial data as an NFT on the blockchain. This makes data tampering difficult, improving the reliability and security of the user's facial data. For example, the registration unit stores the user's 3D facial data as a unique and valuable NFT, making it difficult to tamper with. This allows users to confirm that their facial data is securely stored and use the system with peace of mind. The registration unit also minimizes the risk of data leakage by encrypting and storing the user's facial data. Additionally, the registration unit provides an interface that allows users to manage their facial data, enabling them to update or delete data as needed. This allows users to keep their data up-to-date at all times.

[0077] The monitoring unit monitors the internet. For example, the monitoring unit constantly monitors sites such as social media and blogs to check whether user photos have been uploaded. Specifically, the monitoring unit uses AI to analyze vast amounts of data on the internet in real time and detect user photos. The AI ​​uses image recognition technology to scan images on the internet and identify user photos. For example, the monitoring unit checks whether user photos are included in social media posts, blog articles, and images on news sites. The monitoring unit uses AI deep learning algorithms to analyze facial features with high accuracy and can quickly detect user photos. Furthermore, the monitoring unit automatically scans every time new content is uploaded to the internet to check whether it contains user photos. This allows the monitoring unit to detect user photos before they are published on the internet and to respond quickly. Also, if a user's photo is detected, the monitoring unit sends that information to the detection unit to proceed to the next step. This allows the monitoring unit to protect user privacy and prevent the misuse of facial photos on the internet.

[0078] The detection unit detects the facial photographs detected by the monitoring unit. The detection unit can detect user facial photographs with high accuracy, for example, by using AI. Specifically, the detection unit identifies and detects user facial photographs using facial recognition technology. The AI ​​utilizes a facial recognition algorithm based on deep learning to analyze facial features in the image and identify user facial photographs with high accuracy. For example, the detection unit analyzes the image data transmitted from the monitoring unit to check whether it contains a user's facial photograph. The detection unit analyzes the facial contours and features such as eyes, nose, and mouth in detail to identify the user's facial photograph. Furthermore, if a user's facial photograph is detected, the detection unit sends that information to the notification unit and proceeds to the next step. This allows the detection unit to detect user facial photographs with high accuracy before they are published on the internet and to respond quickly. In addition, the detection unit can improve the accuracy of facial photograph detection by utilizing past data and statistical information. For example, it can continuously train the facial recognition algorithm based on data of facial photographs detected in the past to improve detection accuracy. This allows the detection unit to always use the latest technology to perform highly accurate facial image detection, thereby protecting user privacy.

[0079] The notification unit notifies the user of information detected by the detection unit. The notification unit sends notifications to the user, for example, using email or app notifications. Specifically, the notification unit sends notifications in an appropriate manner based on the user's contact information. For example, it can send notifications to the email address registered by the user or send push notifications through a dedicated application. The notification unit sends notifications that include information on who is trying to post the user's face photo on which site. This allows the user to understand how their face photo is being used. Furthermore, the notification unit provides detailed information in the notification to help the user take appropriate action. For example, the notification may include the date and time the face photo was detected, the URL of the site, and information about the poster. This allows the user to respond quickly and take necessary measures. The notification unit can also collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, it can track the user's response after receiving a notification and identify areas for improvement in the notification content. This allows the notification unit to provide users with timely and accurate information and protect their privacy.

[0080] The permission unit accepts user permission based on information notified by the notification unit. For example, the permission unit provides an interface where users can choose whether or not to allow posting. Specifically, the permission unit provides an interface where users can accept permission to post by selecting "yes" or "no". For example, after a user receives a notification, they can choose to allow posting through a dedicated application or website. The permission unit records the user's selection and proceeds to the next step. This allows users to decide for themselves whether or not their face photo will be published on the internet. Furthermore, the permission unit manages the posting permission status based on the user's selection. For example, if the user selects "yes", the information is recorded and it is confirmed that posting has been permitted. Conversely, if the user selects "no", the information is recorded and it is confirmed that posting has not been permitted. This allows the permission unit to take appropriate action based on the user's selection and protect the user's privacy. The permission unit also provides a function to update selections in case the user wishes to change their selection. This allows users to review their selections at any time and change them as needed.

[0081] The blocking unit blocks posts that have not been permitted by the permission unit. For example, if a user selects "No," the blocking unit will block that post. Specifically, the blocking unit uses AI to prevent users' photos from being published on the internet. The AI ​​monitors internet posts, identifies posts containing users' photos, and blocks them. For example, the blocking unit requests sites such as social networking services and blogs to remove posts containing users' photos. The blocking unit also monitors internet posts in real time and responds quickly if a new post containing a user's photo is uploaded. This allows the blocking unit to prevent users' photos from being published on the internet and protect user privacy. Furthermore, the blocking unit verifies whether posts containing users' photos have been removed and notifies the user when the removal is complete. This allows users to confirm that their photos have been removed and use the internet with peace of mind. The blocking unit also has a function to respond if a removal request is refused. For example, if a removal request is refused, the blocking unit collects information for taking legal action and provides it to the user. This allows the blocking function to prevent the unauthorized use of users' facial images and provide comprehensive measures to protect user privacy.

[0082] The monitoring unit can monitor sites such as social networking services (SNS) and blogs. For example, the monitoring unit can constantly monitor SNS platforms and blog services to check whether user photos have been uploaded. The monitoring unit uses AI to analyze vast amounts of data on the internet in real time and detect user photos. This allows for broad coverage of places where user photos may be uploaded by monitoring sites such as SNS and blogs. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input data from SNS platforms and blog services into a generating AI and have the generating AI perform the detection of user photos.

[0083] The detection unit can detect information when a user's face photo is about to be posted. The detection unit can, for example, use AI to detect situations in which a user's face photo is about to be posted. For example, the detection unit can detect a user's face photo when the post button is pressed or when the post content is uploaded. This allows for a quick response by detecting information when a user's face photo is about to be posted. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input data on situations in which a post is about to be posted into a generating AI and have the generating AI perform the detection of the user's face photo.

[0084] The notification unit can notify the user of information about who is trying to post the user's face photo on which site. The notification unit sends notifications to the user, for example, using email or app notifications. The notification unit sends notifications that include information such as the poster's username and the URL of the site. This allows the user to take appropriate action by notifying the user of information about who is trying to post the user's face photo on which site. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the detected information into a generating AI and have the generating AI generate the notification content.

[0085] The permission unit can provide an interface that allows the user to choose whether or not to allow a post. For example, the permission unit can provide a web interface or a mobile app interface, and the user can choose "yes" or "no" to grant permission for the post. By providing an interface that allows the user to choose whether or not to allow a post, it becomes possible to respond in a way that reflects the user's wishes. Some or all of the above processing in the permission unit may be performed using AI or not. For example, the permission unit can input the user's selection data into a generating AI and have the generating AI make the permission decision.

[0086] The blocking unit can block a post if the user selects "No". For example, if the user selects "No", the blocking unit will automatically block the post. The blocking unit uses AI to prevent the user's face photo from being published on the internet. This prevents the user's face photo from being published on the internet by blocking the post when the user selects "No". Some or all of the above processing in the blocking unit may be performed using AI or not. For example, the blocking unit can input the user's selection data into a generating AI and have the generating AI perform the blocking operation.

[0087] The registration unit can estimate the user's emotions and adjust the timing of 3D facial data registration based on the estimated emotions. For example, if the user is relaxed, the registration unit can send a notification prompting the user to register 3D facial data. The registration unit can also set a reminder for the user to register later if they are busy. Furthermore, if the user is stressed, the registration unit can simplify the registration process and complete it quickly. This reduces the user's burden by adjusting the timing of 3D facial data registration based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's emotion data into a generative AI and have the generative AI adjust the registration timing.

[0088] The registration unit can analyze the user's past facial photograph data and select the optimal method for generating 3D data. For example, the registration unit can select the clearest image from the user's past facial photograph data and generate 3D data. The registration unit can also analyze the user's facial features, select the optimal angle and expression, and generate 3D data. Furthermore, the registration unit can generate 3D data of the face under different lighting conditions based on the user's past facial photograph data. In this way, optimal 3D data can be generated by analyzing the user's past facial photograph data. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's past facial photograph data into a generation AI and have the generation AI select the optimal method for generating 3D data.

[0089] The registration unit can filter 3D facial data based on the user's current lifestyle and areas of interest. For example, if the user is interested in sports, the registration unit can apply sports-related filters to generate 3D data. Furthermore, if the user wishes to use the data in a business setting, the registration unit can generate 3D data reflecting formal expressions and attire. Additionally, if the user is interested in entertainment, the registration unit can generate 3D data reflecting cheerful expressions and poses. This allows for the generation of more appropriate 3D data by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the registration unit may be performed using AI, or not. For example, the registration unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0090] The registration unit can estimate the user's emotions and determine the priority of 3D facial data to register based on the estimated emotions. For example, if the user is relaxed, the registration unit will prioritize registering detailed 3D data. If the user is in a hurry, the registration unit can also prioritize registering simplified 3D data. Furthermore, if the user is excited, the registration unit can prioritize registering specific facial expressions or poses. This allows for data registration tailored to the user's situation by prioritizing 3D data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0091] The registration unit can prioritize registering highly relevant data when registering 3D facial data, taking into account the user's geographical location. For example, if the user lives in a specific region, the registration unit can prioritize registering 3D data that matches the culture and customs of that region. Furthermore, if the user is traveling, the registration unit can prioritize registering 3D data that reflects the characteristics of the travel destination. Additionally, if the user is participating in a specific event, the registration unit can prioritize registering 3D data related to that event. This allows for the priority registration of highly relevant data by considering the user's geographical location. Some or all of the above processing in the registration unit may be performed using AI, or not. For example, the registration unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0092] The registration unit can analyze the user's social media activity and register relevant data when registering 3D facial data. For example, the registration unit can register relevant 3D data based on the content the user frequently posts on social media. The registration unit can also analyze the activity of the user's social media followers and friends and register relevant 3D data. Furthermore, if the user uses a specific hashtag, the registration unit can register 3D data related to that hashtag. In this way, relevant data can be registered by analyzing the user's social media activity. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input data on the user's social media activity into a generating AI and have the generating AI select relevant data.

[0093] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can increase the monitoring frequency. Conversely, if the user is relaxed, the monitoring unit can decrease the monitoring frequency. Furthermore, if the user is busy, the monitoring unit can appropriately adjust the monitoring frequency. This allows for monitoring tailored to the user's situation by adjusting the monitoring frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring frequency.

[0094] The monitoring unit can adjust the monitoring intensity for specific sites or platforms during monitoring. For example, the monitoring unit can increase the monitoring intensity for social networking sites. It can also adjust the monitoring intensity for blogging platforms. Furthermore, the monitoring unit can appropriately set the monitoring intensity for video sharing sites. This allows for efficient monitoring by adjusting the monitoring intensity for specific sites or platforms. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input data from specific sites or platforms into a generating AI and have the generating AI perform the adjustment of the monitoring intensity.

[0095] The monitoring unit can analyze the user's past internet activity during monitoring and select the optimal monitoring method. For example, the monitoring unit can prioritize monitoring sites that the user frequently accesses. Furthermore, the monitoring unit can identify high-risk sites based on the user's past internet activity and strengthen monitoring accordingly. In addition, the monitoring unit can analyze patterns in the user's internet activity and set an optimal monitoring schedule. This allows for the selection of the optimal monitoring method by analyzing the user's past internet activity. Some or all of the above processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the user's past internet activity data into a generating AI and have the generating AI select the optimal monitoring method.

[0096] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise display method. By adjusting the display method of the monitoring results based on the user's emotions, a user-friendly display can be achieved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0097] The monitoring unit can adjust its monitoring range while taking into account the user's geographical location information. For example, if the user is in a specific region, the monitoring unit will prioritize monitoring sites related to that region. Furthermore, if the user is traveling, the monitoring unit can include sites in the travel destination in its monitoring range. Additionally, if the user is participating in a specific event, the monitoring unit can include sites related to that event in its monitoring range. This allows for more relevant monitoring by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI adjust the monitoring range.

[0098] The monitoring unit can analyze a user's social media activity during monitoring to select targets for monitoring. For example, the monitoring unit may prioritize monitoring social media platforms to which the user frequently posts. It can also analyze the activity of the user's social media followers and friends and include related sites in its monitoring targets. Furthermore, if the user uses a specific hashtag, the monitoring unit can include sites related to that hashtag in its monitoring targets. This allows for the selection of relevant targets by analyzing the user's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input data on the user's social media activity into a generating AI and have the generating AI perform the selection of targets for monitoring.

[0099] The detection unit can estimate the user's emotions and adjust the detection accuracy based on the estimated emotions. For example, if the user is feeling anxious, the detection unit can increase the detection accuracy. It can also appropriately adjust the detection accuracy if the user is relaxed. Furthermore, if the user is busy, the detection unit can appropriately set the detection accuracy. This allows for detection tailored to the user's situation by adjusting the detection accuracy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the detection unit may be performed using AI or not. For example, the detection unit can input user emotion data into the generative AI and have the generative AI adjust the detection accuracy.

[0100] The detection unit can improve detection accuracy by analyzing the features of the facial photograph in detail during detection. For example, the detection unit can improve detection accuracy by analyzing the contours and feature points of the face in detail. The detection unit can also improve detection accuracy by considering facial expressions and angles. Furthermore, the detection unit can analyze the lighting conditions of the face and apply the optimal detection algorithm. This improves detection accuracy by analyzing the features of the facial photograph in detail. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the feature data of the facial photograph into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0101] The detection unit can optimize its detection algorithm by referring to the user's past facial image data during detection. For example, the detection unit can select the optimal detection algorithm based on the user's past facial image data. The detection unit can also analyze the user's facial features and optimize the detection algorithm. Furthermore, the detection unit can improve detection accuracy by referring to the user's past facial image data. In this way, the detection algorithm can be optimized by referring to the user's past facial image data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the user's past facial image data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0102] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated emotions. For example, if the user is tense, the detection unit can provide a simple and highly visible display method. If the user is relaxed, the detection unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the detection unit can provide a display method that gets straight to the point. By adjusting the display method of the detection results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the detection unit may be performed using AI or not using AI. For example, the detection unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0103] The detection unit can adjust its detection range while considering the user's geographical location information. For example, if the user is in a specific region, the detection unit will prioritize detecting sites related to that region. Furthermore, if the user is traveling, the detection unit can also include sites in the travel destination in its detection range. Additionally, if the user is participating in a specific event, the detection unit can include sites related to that event in its detection range. This allows for highly relevant detection by considering the user's geographical location information. Some or all of the above processing in the detection unit may be performed using AI, or without AI. For example, the detection unit can input the user's geographical location information into a generating AI and have the generating AI adjust the detection range.

[0104] The detection unit can analyze the user's social media activity and select targets for detection during the detection process. For example, the detection unit may prioritize detecting social media platforms that the user frequently posts on. The detection unit can also analyze the activity of the user's social media followers and friends and include related sites in the detection targets. Furthermore, if the user uses a specific hashtag, the detection unit can also include sites related to that hashtag in the detection targets. This allows the detection unit to select relevant targets by analyzing the user's social media activity. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the selection of detection targets.

[0105] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is relaxed, the notification unit can send a notification immediately. The notification unit can also set a reminder to send a notification later if the user is busy. Furthermore, if the user is stressed, the notification unit can simplify and send the notification quickly. This allows users to receive notifications at the appropriate time by adjusting the timing of notifications based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the notification timing.

[0106] The notification unit can adjust the level of detail in the notification content based on the user's level of interest. For example, if the user shows a high level of interest, the notification unit will provide detailed notification content. Conversely, if the user shows a low level of interest, the notification unit can provide concise notification content. Furthermore, the notification unit can dynamically adjust the level of detail in the notification content according to the user's level of interest. This allows the notification unit to provide the user with information appropriate to their level of interest. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user interest data into a generating AI and have the generating AI perform the adjustment of the level of detail in the notification content.

[0107] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit will prioritize using notification methods that the user has preferred in the past (email, push notifications, etc.). The notification unit can also select the most effective notification method from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and set the optimal notification timing. This allows the notification unit to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0108] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is feeling anxious, the notification unit will prioritize sending important notifications. It can also prioritize sending normal notifications if the user is relaxed. Furthermore, if the user is busy, it can prioritize sending urgent notifications. This ensures that important notifications are received preferentially by prioritizing notifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine notification priorities.

[0109] The notification unit can customize notification content by considering the user's geographical location when sending a notification. For example, if the user is in a specific region, the notification unit can include information related to that region in the notification. It can also include information about the travel destination if the user is traveling. Furthermore, if the user is participating in a specific event, the notification unit can include information related to that event in the notification. This allows for the provision of highly relevant notifications by considering the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI customize the notification content.

[0110] The notification unit can analyze the user's social media activity and adjust the notification content when sending a notification. For example, the notification unit can include relevant information in the notification based on the content the user frequently posts on social media. The notification unit can also analyze the activity of the user's social media followers and friends and include relevant information in the notification. Furthermore, if the notification unit uses a specific hashtag, it can include information related to that hashtag in the notification. This allows the notification unit to provide relevant notification content by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input data on the user's social media activity into a generating AI and have the generating AI adjust the notification content.

[0111] The permission unit can estimate the user's emotions and adjust the permission interface based on the estimated emotions. For example, if the user is relaxed, the permission unit can provide detailed permission options. If the user is in a hurry, it can also provide simplified permission options. Furthermore, if the user is stressed, the permission unit can provide an intuitive and easy-to-use permission interface. This allows for a user-friendly interface by adjusting the permission interface based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the permission unit may be performed using AI or not. For example, the permission unit can input user emotion data into a generative AI and have the generative AI perform the interface adjustments.

[0112] The authorization unit can select the optimal authorization method by referring to the user's past authorization history when granting authorization. For example, the authorization unit can propose the optimal authorization method based on what the user has previously authorized. The authorization unit can also select the most effective authorization method from the user's past authorization history. Furthermore, the authorization unit can analyze the user's past authorization history and set the optimal authorization timing. This allows the authorization unit to select the optimal authorization method by referring to the user's past authorization history. Some or all of the above processing in the authorization unit may be performed using AI or not. For example, the authorization unit can input the user's past authorization history data into a generating AI and have the generating AI perform the selection of the optimal authorization method.

[0113] The permission unit can customize the permission process based on the user's current living situation when granting permission. For example, if the user is busy, the permission unit can provide a simplified permission process. If the user is relaxed, the permission unit can also provide a more detailed permission process. Furthermore, if the user is stressed, the permission unit can provide an intuitive and easy-to-use permission process. This allows the permission unit to provide an appropriate permission process based on the user's current living situation. Some or all of the above processing in the permission unit may be performed using AI or not. For example, the permission unit can input user living situation data into a generating AI and have the generating AI perform the customization of the permission process.

[0114] The permission unit can estimate the user's emotions and determine the priority of permissions based on the estimated emotions. For example, if the user is feeling anxious, the permission unit will prioritize important permissions. It can also prioritize normal permissions if the user is relaxed. Furthermore, if the user is busy, the permission unit can prioritize urgent permissions. This allows for prioritizing important permissions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the permission unit may be performed using AI or not. For example, the permission unit can input user emotion data into a generative AI and have the generative AI determine the priority of permissions.

[0115] The permission unit can customize the permissions given, taking into account the user's geographical location. For example, if the user is in a specific region, the permission unit can provide permissions related to that region. Furthermore, if the user is traveling, the permission unit can include information about the travel destination in the permissions. Additionally, if the user is participating in a specific event, the permission unit can provide permissions related to that event. This allows for the provision of highly relevant permissions by considering the user's geographical location. Some or all of the above processing in the permission unit may be performed using AI or not. For example, the permission unit can input the user's geographical location into a generating AI and have the generating AI customize the permissions.

[0116] The permission unit can analyze the user's social media activity and adjust the permissions when granting access. For example, the permission unit can provide relevant permissions based on the content the user frequently posts on social media. It can also analyze the activity of the user's social media followers and friends and provide relevant permissions. Furthermore, if the permission unit uses a specific hashtag, it can provide permissions related to that hashtag. In this way, relevant permissions can be provided by analyzing the user's social media activity. Some or all of the above processing in the permission unit may be performed using AI or not. For example, the permission unit can input data on the user's social media activity into a generating AI and have the generating AI perform the adjustment of permissions.

[0117] The blocking unit can estimate the user's emotions and adjust the blocking method based on the estimated emotions. For example, if the user is feeling anxious, the blocking unit will block quickly. It can also provide detailed blocking options if the user is relaxed. Furthermore, if the user is busy, it can provide simplified blocking options. This allows the system to provide an appropriate blocking method for the user by adjusting the blocking method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the blocking unit may be performed using AI or not. For example, the blocking unit can input user emotion data into a generative AI and have the generative AI adjust the blocking method.

[0118] The blocking unit can select the optimal blocking method by referring to the user's past blocking history when blocking. For example, the blocking unit can suggest the optimal blocking method based on the content the user has blocked in the past. The blocking unit can also select the most effective blocking method from the user's past blocking history. Furthermore, the blocking unit can analyze the user's past blocking history and set the optimal blocking timing. This allows the optimal blocking method to be selected by referring to the user's past blocking history. Some or all of the above processing in the blocking unit may be performed using AI or not. For example, the blocking unit can input the user's past blocking history data into a generating AI and have the generating AI select the optimal blocking method.

[0119] The blocking unit can customize the blocking method based on the user's current life situation when blocking. For example, if the user is busy, the blocking unit can perform blocking quickly. It can also provide detailed blocking options if the user is relaxed. Furthermore, if the user is stressed, the blocking unit can provide intuitive and easy-to-use blocking methods. This allows the blocking unit to provide the appropriate blocking method for the user by customizing it based on the user's current life situation. Some or all of the above processing in the blocking unit may be performed using AI or not. For example, the blocking unit can input user life situation data into a generating AI and have the generating AI perform the customization of the blocking method.

[0120] The block unit can estimate the user's emotions and determine the priority of blocks based on the estimated emotions. For example, if the user is feeling anxious, the block unit will prioritize important blocks. It can also prioritize normal blocks if the user is relaxed. Furthermore, if the user is busy, the block unit can prioritize urgent blocks. This allows for the priority of important blocks by determining block priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the block unit may be performed using AI or not. For example, the block unit can input user emotion data into a generative AI and have the generative AI determine the priority of blocks.

[0121] The block section can customize the content of a block by considering the user's geographical location information when blocking. For example, if the user is in a specific region, the block section can provide block content related to that region. Furthermore, if the user is traveling, the block section can include information about the travel destination in the block content. Additionally, if the user is participating in a specific event, the block section can provide block content related to that event. This allows for the provision of highly relevant block content by considering the user's geographical location information. Some or all of the above processing in the block section may be performed using AI, or not. For example, the block section can input the user's geographical location information into a generating AI and have the generating AI perform the customization of the block content.

[0122] The blocking unit can analyze the user's social media activity and adjust the blocking content when blocking. For example, the blocking unit can provide relevant blocking content based on the content the user frequently posts on social media. It can also analyze the activity of the user's social media followers and friends and provide relevant blocking content. Furthermore, if the blocking unit uses a specific hashtag, it can provide blocking content related to that hashtag. In this way, relevant blocking content can be provided by analyzing the user's social media activity. Some or all of the above processing in the blocking unit may be performed using AI or not. For example, the blocking unit can input data on the user's social media activity into a generating AI and have the generating AI perform the adjustment of the blocking content.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The registration unit can analyze the user's past facial photograph data and select the optimal method for generating 3D data when registering 3D data of the user's face. For example, it can select the clearest image from the user's past facial photograph data and generate 3D data from it. It can also analyze the user's facial features and select the optimal angle and expression to generate 3D data. Furthermore, it can generate 3D data of the face under different lighting conditions based on the user's past facial photograph data. In this way, optimal 3D data can be generated by analyzing the user's past facial photograph data. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's past facial photograph data into a generation AI and have the generation AI select the optimal method for generating 3D data.

[0125] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is feeling anxious, the monitoring frequency can be increased. Conversely, if the user is relaxed, the monitoring frequency can be decreased. Furthermore, if the user is busy, the monitoring frequency can be appropriately adjusted. This allows for monitoring tailored to the user's situation by adjusting the monitoring frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring frequency.

[0126] The detection unit can improve detection accuracy by analyzing the features of the facial photograph in detail during detection. For example, it can improve detection accuracy by analyzing the contours and feature points of the face in detail. It can also improve detection accuracy by considering facial expressions and angles. Furthermore, it can analyze the lighting conditions of the face and apply the optimal detection algorithm. As a result, detection accuracy is improved by analyzing the features of the facial photograph in detail. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the feature data of the facial photograph into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0127] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is relaxed, a notification can be sent immediately. If the user is busy, a reminder can be set to send the notification later. Furthermore, if the user is stressed, the notification can be simplified and sent quickly. This allows users to receive notifications at the appropriate time by adjusting the timing based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the notification timing.

[0128] The permission unit can estimate the user's emotions and adjust the permission interface based on the estimated emotions. For example, if the user is relaxed, it can provide detailed permission options. If the user is in a hurry, it can provide simplified permission options. Furthermore, if the user is stressed, it can provide an intuitive and easy-to-use permission interface. In this way, by adjusting the permission interface based on the user's emotions, a user-friendly interface can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the permission unit may be performed using AI or not. For example, the permission unit can input user emotion data into a generative AI and have the generative AI perform the interface adjustments.

[0129] The blocking unit can estimate the user's emotions and adjust the blocking method based on the estimated emotions. For example, if the user is feeling anxious, it can block quickly. If the user is relaxed, it can provide detailed blocking options. Furthermore, if the user is busy, it can provide simplified blocking options. This allows the system to provide an appropriate blocking method for the user by adjusting the blocking method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the blocking unit may be performed using AI or not. For example, the blocking unit can input user emotion data into a generative AI and have the generative AI adjust the blocking method.

[0130] The monitoring unit can adjust the monitoring intensity for specific sites or platforms during monitoring. For example, it can increase the monitoring intensity for social networking sites. It can also adjust the monitoring intensity for blogging platforms. Furthermore, it can appropriately set the monitoring intensity for video sharing sites. This allows for efficient monitoring by adjusting the monitoring intensity for specific sites or platforms. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input data from specific sites or platforms into a generating AI and have the generating AI perform the adjustment of the monitoring intensity.

[0131] The detection unit can optimize its detection algorithm by referring to the user's past facial image data during detection. For example, it can select the optimal detection algorithm based on the user's past facial image data. It can also analyze the user's facial features and optimize the detection algorithm. Furthermore, it can improve detection accuracy by referring to the user's past facial image data. In this way, the detection algorithm can be optimized by referring to the user's past facial image data. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the user's past facial image data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0132] The notification unit can adjust the level of detail in the notification content based on the user's level of interest. For example, if the user shows high interest, it can provide detailed notification content. Conversely, if the user shows low interest, it can provide concise notification content. Furthermore, the level of detail in the notification content can be dynamically adjusted according to the user's level of interest. This allows the system to provide the user with information appropriate to their needs by adjusting the level of detail in the notification content based on their level of interest. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user interest data into a generating AI and have the generating AI perform the adjustment of the level of detail in the notification content.

[0133] The authorization unit can select the optimal authorization method by referring to the user's past authorization history when granting authorization. For example, it can propose the optimal authorization method based on what the user has previously authorized. It can also select the most effective authorization method from the user's past authorization history. Furthermore, it can analyze the user's past authorization history and set the optimal authorization timing. This allows the system to select the optimal authorization method by referring to the user's past authorization history. Some or all of the above processing in the authorization unit may be performed using AI or not. For example, the authorization unit can input the user's past authorization history data into a generating AI and have the generating AI select the optimal authorization method.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The registration unit registers the user's 3D facial data. For example, a user can upload their own 3D facial data to the platform. The registration unit can also store the user's 3D facial data as an NFT on the blockchain. This ensures that the user's 3D facial data is stored as a unique and valuable NFT, making it difficult to tamper with. Step 2: The monitoring unit monitors the internet. For example, it constantly monitors sites such as social media and blogs to check whether user photos have been uploaded. The monitoring unit uses AI to analyze vast amounts of data on the internet in real time and detect user photos. Step 3: The detection unit detects the facial image detected by the monitoring unit. For example, AI can be used to detect the user's facial image with high accuracy. The detection unit identifies and detects the user's facial image using facial recognition technology. Step 4: The notification unit notifies the user of the information detected by the detection unit. For example, it sends a notification to the user using email or app notifications. The notification unit sends a notification that includes information about who is trying to post the user's face photo on which site. Step 5: The permission unit accepts user permission based on the information notified by the notification unit. For example, it provides an interface where the user can choose whether or not to allow the post. The user's selection of "yes" or "no" grants permission for the post. Step 6: The blocking unit blocks posts that were not permitted by the allowing unit. For example, if the user selects "No," the post will be blocked. The blocking unit uses AI to prevent the user's face photo from being published on the internet.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0138] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the registration unit, monitoring unit, detection unit, notification unit, permission unit, and blocking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit allows the user's 3D facial data to be uploaded to the platform by the control unit 46A of the smart device 14. The monitoring unit constantly monitors the internet by the identification processing unit 290 of the data processing unit 12 to check whether the user's facial photograph has been uploaded. The detection unit detects the facial photograph detected by the identification processing unit 290 of the data processing unit 12 with high accuracy. The notification unit sends a notification to the user by the control unit 46A of the smart device 14. The permission unit accepts the user's permission by the control unit 46A of the smart device 14. The blocking unit blocks unauthorized posts by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the registration unit, monitoring unit, detection unit, notification unit, permission unit, and blocking unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit allows the user's 3D facial data to be uploaded to the platform by the control unit 46A of the smart glasses 214. The monitoring unit constantly monitors the internet by the identification processing unit 290 of the data processing unit 12 to check whether the user's facial photograph has been uploaded. The detection unit detects the facial photograph detected by the identification processing unit 290 of the data processing unit 12 with high accuracy. The notification unit sends a notification to the user by the control unit 46A of the smart glasses 214. The permission unit accepts the user's permission by the control unit 46A of the smart glasses 214. The blocking unit blocks unauthorized posts by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the registration unit, monitoring unit, detection unit, notification unit, authorization unit, and blocking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit allows the user's 3D facial data to be uploaded to the platform by the control unit 46A of the headset terminal 314. The monitoring unit constantly monitors the internet by the identification processing unit 290 of the data processing unit 12 to check whether the user's facial photograph has been uploaded. The detection unit detects the facial photograph detected by the identification processing unit 290 of the data processing unit 12 with high accuracy. The notification unit sends a notification to the user by the control unit 46A of the headset terminal 314. The authorization unit accepts the user's authorization by the control unit 46A of the headset terminal 314. The blocking unit blocks unauthorized posts by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0174] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0175] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0176] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0177] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0178] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0179] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0180] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0181] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0182] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0183] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0184] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0185] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0186] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0187] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0188] Each of the multiple elements described above, including the registration unit, monitoring unit, detection unit, notification unit, permission unit, and blocking unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the registration unit allows the control unit 46A of the robot 414 to upload 3D data of the user's face to the platform. The monitoring unit, using the identification processing unit 290 of the data processing unit 12, constantly monitors the internet and checks whether the user's face photo has been uploaded. The detection unit detects the face photo detected by the identification processing unit 290 of the data processing unit 12 with high accuracy. The notification unit sends a notification to the user using the control unit 46A of the robot 414. The permission unit accepts the user's permission using the control unit 46A of the robot 414. The blocking unit blocks unauthorized posts using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0189] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0190] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0191] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0192] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0193] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0194] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0195] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0196] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0197] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0198] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0199] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0200] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0201] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0202] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0203] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0204] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0205] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0206] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0207] (Note 1) A registration unit for registering 3D data of the user's face, The monitoring department that monitors the internet, A detection unit for detecting a facial photograph detected by the monitoring unit, A notification unit that notifies the user of the information detected by the detection unit, An authorization unit that accepts user permission based on the information notified by the aforementioned notification unit, The system includes a blocking unit that blocks posts that were not permitted by the permission unit. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Monitoring social media sites and blogs The system described in Appendix 1, characterized by the features described herein. (Note 3) The detection unit is The system detects information when a user's face photo is about to be posted. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Notify users of who is trying to post their photos on which website. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned authorization unit is, Provide an interface that allows users to choose whether or not to allow posts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned block section is Block the post if the user selects "No". The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of registering 3D facial data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is The system analyzes the user's past facial image data and selects the optimal method for generating 3D data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is When registering 3D facial data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is The system estimates the user's emotions and prioritizes the 3D facial data to register based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is When registering 3D facial data, the system prioritizes registering highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is When registering 3D facial data, the system analyzes the user's social media activity and registers relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned monitoring unit, During monitoring, adjust the monitoring intensity for specific sites or platforms. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned monitoring unit, During monitoring, the system analyzes the user's past internet activity to select the most suitable monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned monitoring unit, During monitoring, the monitoring range is adjusted considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned monitoring unit, During monitoring, the system analyzes users' social media activity to select targets for monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is It estimates the user's emotions and adjusts the accuracy of the detection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is During detection, the facial features of the image are analyzed in detail to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is During detection, the detection algorithm is optimized by referring to the user's past facial image data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is It estimates the user's emotions and adjusts how the detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is During detection, the detection range is adjusted considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit is During detection, the system analyzes the user's social media activity to select targets for detection. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When a notification is sent, the level of detail in the notification content is adjusted based on the user's level of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending notifications, customize the notification content based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, we analyze the user's social media activity and adjust the notification content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned authorization unit is, It estimates the user's emotions and adjusts the permission interface based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned authorization unit is, When granting permission, the system will refer to the user's past permission history to select the most appropriate permission method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned authorization unit is, When granting permission, customize the permission method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned authorization unit is, It estimates the user's emotions and determines permission priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned authorization unit is, When granting permission, customize the permissions based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned authorization unit is, When granting permission, the system analyzes the user's social media activity and adjusts the permissions accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned block section is It estimates the user's emotions and adjusts the blocking method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned block section is When blocking a user, the system will refer to the user's past blocking history to select the most appropriate blocking method. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned block section is When blocking a user, the blocking method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned block section is It estimates the user's emotions and determines the priority of blocking based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned block section is When blocking, the content of the block will be customized based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned block section is When blocking a user, the system analyzes their social media activity to adjust the blocking process. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0208] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A registration unit for registering 3D data of the user's face, The monitoring department that monitors the internet, A detection unit for detecting a facial photograph detected by the monitoring unit, A notification unit that notifies the user of the information detected by the detection unit, An authorization unit that accepts user permission based on the information notified by the aforementioned notification unit, The system includes a blocking unit that blocks posts that were not permitted by the permission unit. A system characterized by the following features.

2. The aforementioned monitoring unit, Monitoring social media sites and blogs The system according to feature 1.

3. The detection unit is The system detects information when a user's face photo is about to be posted. The system according to feature 1.

4. The aforementioned notification unit, Notify users of who is trying to post their photos on which website. The system according to feature 1.

5. The aforementioned authorization unit is, Provide an interface that allows users to choose whether or not to allow posts. The system according to feature 1.

6. The aforementioned block section is Block the post if the user selects No. The system according to feature 1.

7. The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of registering 3D facial data based on the estimated emotions. The system according to feature 1.

8. The aforementioned registration unit is The system analyzes the user's past facial image data and selects the optimal method for generating 3D data. The system according to feature 1.

9. The aforementioned registration unit is When registering 3D facial data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned registration unit is The system estimates the user's emotions and prioritizes the 3D facial data to register based on the estimated emotions. The system according to feature 1.

Images